torchaudio.info returns incorrect result for num_frames when input is a video #3731
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Description
🐛 Describe the bug
If the input is a video, torchaudio.info(<path>).num_frames returns the incorrect result. For example:
import torchaudio
from subprocess import check_call
url = "https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.mp4"
check_call(["wget", url, "-O", "sample_video.mp4"])
path = "sample_video.mp4"
wav, sr = torchaudio.load(path)
print(wav.shape)
print(torchaudio.info(path).num_frames)Prints torch.Size([2, 9889792]) for wav.shape, but prints 9659 for torchaudio.info(path).num_frames. I'm guessing it's returning the number of video frames instead?
Versions
Collecting environment information...
PyTorch version: 2.1.0
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.27.7
Libc version: glibc-2.31
Python version: 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:40:32) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-1048-aws-x86_64-with-glibc2.31
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
Address sizes: 46 bits physical, 48 bits virtual
CPU(s): 96
On-line CPU(s) list: 0-95
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz
Stepping: 7
CPU MHz: 2499.998
BogoMIPS: 4999.99
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 1.5 MiB
L1i cache: 1.5 MiB
L2 cache: 48 MiB
L3 cache: 71.5 MiB
NUMA node0 CPU(s): 0-23,48-71
NUMA node1 CPU(s): 24-47,72-95
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed: Vulnerable
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke
Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.23.5
[pip3] perceiver-pytorch==0.8.8
[pip3] pytorch-lightning==2.1.2
[pip3] torch==2.1.0
[pip3] torch-fidelity==0.3.0
[pip3] torchaudio==2.1.0
[pip3] torchdiffeq==0.2.3
[pip3] torchlibrosa==0.1.0
[pip3] torchmetrics==1.2.0
[pip3] torchvision==0.16.0
[pip3] triton==2.1.0
[conda] blas 2.116 mkl
[conda] blas-devel 3.9.0 16_linux64_mkl
[conda] libblas 3.9.0 16_linux64_mkl
[conda] libcblas 3.9.0 16_linux64_mkl
[conda] liblapack 3.9.0 16_linux64_mkl
[conda] liblapacke 3.9.0 16_linux64_mkl
[conda] mkl 2022.1.0 h84fe81f_915
[conda] mkl-devel 2022.1.0 ha770c72_916
[conda] mkl-include 2022.1.0 h84fe81f_915
[conda] numpy 1.23.5 pypi_0 pypi
[conda] perceiver-pytorch 0.8.8 pypi_0 pypi
[conda] pytorch 2.1.0 aws_py3.10_cuda11.8_cudnn8.7.0_0
[conda] pytorch-cuda 11.8 h7e8668a_3
[conda] pytorch-lightning 2.1.2 pypi_0 pypi
[conda] pytorch-mutex 1.0 cuda
[conda] torch-fidelity 0.3.0 pypi_0 pypi
[conda] torchaudio 2.1.0 py310_cu118
[conda] torchdiffeq 0.2.3 pypi_0 pypi
[conda] torchlibrosa 0.1.0 pypi_0 pypi
[conda] torchmetrics 1.2.0 pypi_0 pypi
[conda] torchtriton 2.1.0 py310
[conda] torchvision 0.16.0 py310_cu118
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